def sigmoid(x):
    return 1/(1+numpy.exp(-x))
import numpy
from sklearn.linear_model import LogisticRegression
classifer=LogisticRegression(max_iter=10000,solver='sag')
classifer.fit(numpy.array([[1,0],[0,1],[0,2],[1,2],[3,1]]),numpy.array([0,0,1,1,1]))
print(classifer.predict(numpy.array([[1,1]])))
